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ISSN: 1694-2108 | Vol. 2, No. 1. JUNE 2013 1
Negotiation Based Resource
Allocation for Distributed
Environments
Krishnamoorthy M, Senthil Murugan B
School of Information Technology and Engineering, VIT University, Vellore, Tamil nadu, India
ABSTRACT
Resource allocation is an important task in distributed systems. A dynamic allocation is most effective task in decreasing execution time and improving overall system performance. The request for the resources might be generated at any time in environment and it has to be allocated without any delays and conflicts. The workload distributed among the system raises the major concern in the context of optimal allocation of resources. The primary objective of this system is to achieve the high performance and availability. Thereby reducing the computation and communication delays happens in dynamic environment. It generates good results in synchronization between the systems.
Keywords
Resource Allocation, Negotiation, APR protocol, Load balancing, Airplane rerouting.
1. INTRODUCTION
Nowadays we are using computing platform for various purposes. The purpose and usage may differ from one business to other business. Based on their usage level, the appropriate computing hardware is chosen and the users try to optimize its usage, thereby obtaining maximum profit from the minimal hardware. Distributed system provides the environment to process large amount of data with a short span of time. The total workload is distributed among the systems, then it is processed within small amount of time and its results could be aggregated. The job allocation can take place based upon the resource availability. But if the system gets overloaded with more number of jobs then it will create unnecessary delays and traffic, hence there is also a delay in executing jobs submitted before. This results in load imbalance problem and conflicts. So the load can be properly distributed, and the jobs must be completed without any resource conflicts and delays.
ISSN: 1694-2108 | Vol. 2, No. 1. JUNE 2013 2 2. RELATED WORK
Load balancing has been previously studied mainly for the purpose of balancing workload in centralized environment. So we remember some balancing concept for distributed environment which is presented in some papers. In contextual based resource allocation they viewed the system in terms of physical and social contexts. Resource allocations is done based on it maximal usage. In this system, if the task enters into the system, it receives and puts into agent waiting queue. If the waiting queue reaches maximum limit, it stops further allocations. The agents can execute the jobs with required resources. If not, agents try to negotiate with other agents to accomplish their desired tasks. The aim of this approach is to reduce average queues size for the agents and maximizes the resource usage [1].
In Agent based simulations they have to proper monitoring and migrating mechanism to correct load problems. Initially it detects heterogeneity of system connected in environment and its loads. Balancing operations is performed through various phases. Namely, Monitoring phase, filtering and selecting operation phase, redistribution phase and migration phase. Better approach to handle the dynamic changes, happen due to unanticipated resources enters into the system. But we have more computation and communication load to handle and need complex structure to monitoring resources [2].
We have adaptive algorithm to effectively schedule and manage the shared resources. And the failures can be handled very easily. If the main container fails, it replicated main containers to become active. The problem of centralized system fails or overload at centralized system then it totally becomes imbalanced and recovery takes more time. This problem is solved using distributed system through approximate optimized scheduling algorithm with partial information. It is trying to reduce the delays happening between the communication agents. This model can increase distributed simulation performance by minimizing is communication loads [3].
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ISSN: 1694-2108 | Vol. 2, No. 1. JUNE 2013 3 Pipelined computation helps in giving the necessary information and it has been passed from one system to other systems. The pipelined computation is implemented in both centralized and distributed environment. The distributed adaptive scheduling algorithm provides the better load balance compared to centralized systems. The distributed, heterogeneous and shared nature of large system gives more results to complete the work within stipulated amount of time [5].
Load balancing is well handled in agent based distributed system. Here they are tried to reduce the load from heavyweight to light weighted loads. Credit based system is used to handle overload that may happens in the environment. Through regression analysis method we can find out the system information can find out parameters Location credit (LC) and Load balancing coordinator (LBC), that decide when to migrate the load between hosts The overall complexity of this approach is O (n2) [6].
In another approach they are presented the load balancing in game theory point of view. Workload is distributed using round robin algorithm (it allocates equal time slice to execute each task). The local interaction with neighbours is the way to reduce the communication delays. The model which followed here is to assigns the job each node with equal amount of capacities to run. If the node finds overload it tries move the work to any other node with load is very less by giving some payments. Dynamic load balancing is achieved through local interactions itself [7].
3. PROPOSED WORK
3.1 Resource Allocation for Distributed Environment
Before getting detail into our proposed work, let see some of the problems which might occurs in airspace management. In airspace management, we have many numbers of airplanes who takes the journey in particular region. But here, the unmanned little air planes need a protocol to negotiate the route or in software applications where the multiagent system must suggest the user possible alternative reorganizations of the route of many planes. Regarding the airspace management, an accurate research has been made by the “Agent technology centre” that developed AGENTFLY, “a multi-agent system enabling large-scale simulation of civilian and unmanned air traffic. This system integrates advanced flight path planning, decentralized collision avoidance with highly detailed models of the airplanes and the environment”. The AGENTFLY project is still maintained and its authors are continuously improving it in many ways.
ISSN: 1694-2108 | Vol. 2, No. 1. JUNE 2013 4 workload among the systems properly and maintain the system from load imbalance state. Each system must be operated with equal amount of workload. The allocation of jobs is based upon the resource requirement level. The allocation must to provide more benefit when we do the allocation process as well as the reallocation process. It helps in faster execution of jobs submitted. And the resource allocation in distributed environment approach gives more benefits in getting maximum resource usage level.
So the implementation of airspace management is possible to do with kind of distributed environment. The multiagent concept is an easiest approach to make the negotiation between the airplane (Agents) systems. If we want to say accurately, it provides more advantage over the communication and for further operations.
3.2 NegotiationBasedResourceAllocationfor DistributedEnvironment
Negotiation is a concept in distributed multiagent system. The main purpose of negotiation is to reach an agreement, and in particular, agreement in the presence of conflicting goals and preferences. Negotiation usually proceeds in a series of rounds, with some proposal made at every round. Proposal is an agreement defined by the agent to avoid the conflicts. With the help of reallocating the resources among one another, agents gets the mutually benefit. Consider the negotiation with the context of resource allocation in distributed environment. Allocation has to reduce the conflicts between the systems and increase the throughput of the overall system. The resource allocation is defined as tuple of:
<A, Z, v1...vn> Where:
A is a set of agents Z is a set of resources
vi: 2Z Ris a valuation function for each agent i belong to A.
The allocation of Z to A is a partition Z1, Z2 … Zn of the resources Z over the agents A. Now, starting from some initial allocation P0= Z1, Z2… Zn agents can bargain with each other in an attempt to improve the value of their holding. If I have a resource that I do not value highly but you do, and you have resource that you do not value highly but I do, then exchanging resources, we can mutually benefit. Formally we considered for each agent i as making a payment pi. In order to do the new allocation, i can be given some payment, sufficient to compensate for the resulting loss in utility.
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ISSN: 1694-2108 | Vol. 2, No. 1. JUNE 2013 5 1. We start off with the initial allocation Z0; this is the initial allocation
of resources to agents.
2. We define the current allocation to be Z0 with 0 side payments.
3. Any agents is permitted to put forward a deal (Z, Z’, p’) as a proposal where Z is the current allocation. If all agents to that deal (i.e. are satisfied with the allocation Z’ and payments p’) and the relevant termination condition has been satisfied, then negotiation terminates and the deal Z’ is implemented with payments p’.
4. If every agent to the deal but the termination condition is not reached, then the current allocation is set to Z’ with payments p’, and go to step3.
5. If some agent is unsatisfied with deal, then the current allocation remains is unchanged and go to step3.
3.3Comparsion Mechanism for Finding theCollision State
The system which is prevents from collision state is started. All the airplane systems which is moving state, starts signalling to the base systems. The base system is having some algorithm for finding the collision state. The algorithm for finding the collision state is described below with pseudo code. The comparator interface is always keep checks the condition whenever the airplane is in signalling state (Airplane system is in moving state).
Procedure Comparator_Interface (A: list of Agents) Repeat
Flag = false
For it.next () to length (A) inclusive do: For it.next () to length (A-1) inclusive do: Count++
If A [i-1].location ==A [j].location then If A [i-1].height==A [j].height then
If A [i-1].speed==A[j].speed then Add (A [i-1] to List);
ISSN: 1694-2108 | Vol. 2, No. 1. JUNE 2013 6
End if End if End for List ();
For k=0 to length (count) It.previous
End for
Until End of list End procedure
At this juncture, the loop is executed infinitely till the touches the condition, there is no airplane is in Service Agents region. It predominately repeats this process to avoid the collisions. The list A is continually getting updated and traversed with each and every agent, to detect the airplane colliding state. If the loop is traversed once, it activates the handler and manger to continue further process.
4. SYSTEM ARCHITECTURE
In this section, we discuss the architecture of negotiation based resource allocation system. The main reason behind this architecture is to reduce the conflict that arises between the airplane systems.
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ISSN: 1694-2108 | Vol. 2, No. 1. JUNE 2013 7 Figure 1. Data flowing airplane system
The vital role of service agent is to check the conditions for the clashes and to inform corresponding plane agents. This operation is performed by the service handler component. And it continually checks the information provided the process request component. If it finds the collision then it adds to collision list, and it triggers the manager component starts its further operations. The basement makes the negotiation process with the conflicting airplane systems. The negotiation can take place with the context of resource (i.e. route) and payments. The negotiation process starts in the form of communication. Here the four types of messages considered such as INFORM, REQUEST, and PROPOSE, ACCEPT_PROPOSAL and REJECT_PROPOSAL. After finishing the negotiating process the airplane get clear route that never conflicts with any other airplane systems.
5.EXPERIMENTS
ISSN: 1694-2108 | Vol. 2, No. 1. JUNE 2013 8 system to avoid collisions. This system is implemented with distributed multiagent system JADE (Java Agent Development Framework). JADE provides the distributed agent platform. The agent platform can be split among several hosts. Only one Java application, and therefore only one JVM, is executed on each host.Agents are implemented as Java threads and live within Agent Containersthat provide the runtime support to the agent execution. The computer is present at each airplane system helps to load and run the agents. Agents extract the system information (i.e. airplane information) and then it properly sends its signal information to basement systems.
The system which is present at middle named as Service Agent and also considered as basement system, and its keep on receiving the signal from airplane systems. It not only manages all airplane system information and also it does the route allocating process. It keep tracks each airplane to avoiding clashes, and to maintain respecting constraints on the path they can use, time to make a decision, distance they need to maintain between them and so on.
Here we considered airspace as resource which is to be used by the planes to make the journey. All the airplane system (agents) information is got synchronized with service agent in the form of messages. The reason behind synchronizing is to detect the location of planes. At every time it updates the information about airplane system in the servicing agent system.
It always checks the algorithmic conditions for collision occurrence. The condition for finding collision state is comparing the distance and height between the planes including with its speed. If it satisfies the collision state condition, servicing agent informs to corresponding agents. For instance, the distance difference between the airplane systems is less than 25miles is considered for the collision and informed. Negotiations are fully handled by Manager Component. Then corresponding agents receives the INFORM message and immediately it requests the servicing agent for route clearance. Here the purpose of negotiation comes between the agents. The servicing agent responds with REQUEST message contains the proposals. The proposal includes system (route) information to modify and side payment information. The PROPOSE message may contains either ACCEPTED/REJECTED conditions.
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ISSN: 1694-2108 | Vol. 2, No. 1. JUNE 2013 9 and it must be accepted by the airplane agent and to make the payments. Because reject again and again it never the benefit of this maximal usage.
The benefit behind the both proposals is somewhat advantages to either service agent or airplane system. But if agent accepts the proposal at first gets more advantage and the agent rejects the proposal gets less benefit only. Consider we have many number of airplanes may enters into the condition of collision. It has been well handled by the process request and service handler components. At this point the servicing agent is standing as more reliable and available one to manage any number of airplane systems data.
6. RESULTS
The system implementation is made with interconnection of aircraft systems (i.e. Remote system) with service agent. At each system the airplane system information is get loaded. After the start up, it sends current aeroplane system location information to service agent and the corresponding plane information are updated in service agent database. Basically the service agent keeps tracks the aeroplanes for collision, so it must to have updated airplane location information. Since the airplane is moving object, so we can’t predict the data exactly at all the intervals.
Figure 2. Load performance
ISSN: 1694-2108 | Vol. 2, No. 1. JUNE 2013 10 variation. It gives what we already defined in case of 100 systems. The results is slightly may vary due to load performance characteristics. Using this approach get more benefits in low memory consumption, because if airplane reaches its destination, related about the airplane system state is get removed the service agent. So there is no pointless to maintain information about the airplanes, after the end of the journey.
This experiment provides good results with certain number of systems. We connect as many number of service agent which we need when service reaches maximum capability. So here, we have considered one service agent for each sector or region. So we concluded the airplane systems may contact its nearest service agent to get the service. With this formation of several service agents we don’t get any unreachable conditions or not able to connect situation. Gradually, we can get better performance compare to single service agent.
For instance, at the figure 2, we put limits the number of airplane systems for each service agent to connect. At first instant, we are connected 50 to 150 at one service agent. And if requests go beyond 200, it activates another service agent for further requests processing of airplane system. Based on the requirements, we can increase the service agents to get better performance. But we don’t have that much problems, because it maintains the airplane systems information without redundancy.
7. CONCLUSION
The simulation experiment demonstrates the effectiveness of this approach. Our on-going work includes distributed quality optimization for effectively handling the conflicts and error handling mechanisms. As well as information exchange mechanisms among the agents are handled well. We can use this approach for load balancing between the servers and resource allocation. So we have the possibility of sharing computer resources in networking system. It helps to negotiate for obtaining the CPU, RAM, PRINTER, and other computing resources. The main advantage behind this distributed multiagent system approach is the better way to navigate the workload if it reaches maximum limit. And it is easier to maintain the location information in different system (i.e. in its corresponding region of airplane systems), with replication of servicing agent in each sector or region of traffic control system. And the distributed multiagent system provides environment to handle any kind of failure that happens between the systems.
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ISSN: 1694-2108 | Vol. 2, No. 1. JUNE 2013 11 systems must have to proper devices to make communications. At any point if we miss or lose signal information, we got severe problem.
8. REFERENCES
[1] Yichuan Jiang, Member, IEEE, and Jiuchuan Jiang, “Contextual Resource Negotiation-Based Task Allocation and Load Balancing in Complex Software Systems”, IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, VOL. 20, NO.5, MAY 2009.
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[4] Yichuan Jiang AB, Zhaofeng Li C, “Locality-sensitive task allocation and load balancing in networked multiagent systems: Talent versus centrality”, Journalof Parallel Distributed computing 71 (2011) 822-836.
[5] IoannisRiakiotakis A, Florina M. Ciorba B, Theodore Andronikos C, George Papakonstantinou A, “Distributed dynamic load balancing for pipelined computations on heterogeneous systems”, Parallel Computing 37 (2011) 713-729. [6] Maha A. Metaweia B, Salma A. Ghoneim A, Sahar M.Haggaga, Salwa M. Nassar
B, “Load balancing in distributed multiagent computing system”, Ain Shams Engineering journal (2012) 3,237-249.
