A game-theoretic model for dynamic load balancing in distributed systems

Aote, Shailendra S.; Kharat, M. U.

doi:10.1145/1523103.1523153

Cited by 22 publications

(5 citation statements)

References 9 publications

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Section: Introductionsupporting

confidence: 86%

“…On each link, the latency is constant, i.e., it does not vary with the number of sent requests [37]. This assumption is consistent with the previous works on geographically load balancing [4,10,20,21,26,35,37]. Individual requests are small; rather than an hour-long batch job, a request models, e.g., a single web page hit.…”

Section: Introductionsupporting

confidence: 80%

“…Some papers analyze the game-theoretical aspects of load balancing in geographically distributed systems [2,4,20,21,35]. These works use a similar model, but focus on capturing the economic relation between the participating entities.…”

Section: Introductionmentioning

confidence: 99%

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Geographically Distributed Load Balancing with (Almost) Arbitrary Load Functions

Skowron

Rządca

2015

2015 IEEE 22nd International Conference on High Performance Computing (HiPC)

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In geographically-distributed systems, communication latencies are non-negligible. The perceived processing time of a request is thus composed of the time needed to route the request to the server and the true processing time. Once a request reaches a target server, the processing time depends on the total load of that server; this dependency is described by a load function.We consider a broad class of load functions; we just require that they are convex and two times differentiable. In particular our model can be applied to heterogeneous systems in which every server has a different load function. This approach allows us not only to generalize results for queuing theory and for batches of requests, but also to use empirically-derived load functions, measured in a system under stress-testing.The optimal assignment of requests to servers is communication-balanced, i.e. for any pair of non perfectly-balanced servers, the reduction of processing time resulting from moving a single request from the overloaded to underloaded server is smaller than the additional communication latency.We present a centralized and a decentralized algorithm for optimal load balancing. We prove bounds on the algorithms' convergence. To the best of our knowledge these bounds were not known even for the special cases studied previously (queuing theory and batches of requests). Both algorithms are any-time algorithms. In the decentralized algorithm, each server balances the load with a randomly chosen peer. Such algorithm is very robust to failures. We prove that the decentralized algorithm performs locally-optimal steps.Our work extends the currently known results by considering a broad class of load functions and by establishing theoretical bounds on the algorithms' convergence. These results are especially applicable for servers whose characteristics under load cannot be described by a standard mathematical models.

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Section: Introductionsupporting

confidence: 86%

Section: Introductionsupporting

confidence: 80%

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Geographically Distributed Load Balancing with (Almost) Arbitrary Load Functions

Skowron

Rządca

2015

2015 IEEE 22nd International Conference on High Performance Computing (HiPC)

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“…It is mainly introduced for distributed computing and later it has been popular in cloud computing too. A dynamic load balancing based on game theory is proposed by Aote and Khara [3]. In this method, users being decision makers in non-cooperative game.…”

Section: Algorithm 2 Calculation Of Load Degreementioning

confidence: 99%

Round Robin with Load Degree: An Algorithm for Optimal Cloudlet Discovery in Mobile Cloud Computing

Ramasubbareddy¹,

Sasikala²

2018

SCPE

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Mobile devices have become essential in our daily lives but it has limited resources such as battery life, storage, and processing capacity. Offloading resource intensive task into the cloud is an efficient approach to improve battery utilization, storage capacity and processing capabilities. Efficiently computing using cloud resources to process offloaded task in order to improve response time and reduce both tasks' waiting time and latency problems is one of the main goals in mobile cloud computing (MCC). In order to improve user satisfaction and performance of the mobile application, a cloudlet framework concept has been developed to reduce latency problems which improve response time. The cloudlet brings the cloud closer to the user to perform a computational task. This article proposes a new balancing model among cloudlets in mobile cloud computing environment to find the required resources and create an impact on performance. The efficient load balancing model makes mobile cloud computing more attractive and improves user satisfaction. This paper introduces a Round Robin with Load degree algorithm for public cloudlets in mobile cloud computing using a switch mechanism to choose different approaches for different situations. This algorithm uses game theory based load balancing approach to improve application response time in public mobile cloud environments.

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“…This approach avoids bottlenecks, reduces the response time, and improves the system scalability. There are many types of load balancing based on different technologies [43][44][45][46][47][48][49][50][51][52]. • Edge federation: Edge federation enables collaboration and resource sharing between multiple fog nodes or edge networks [53].…”

mentioning

confidence: 99%

Reliablity and Security for Fog Computing Systems

Shiriaev,

Ermakova,

Bezuglova

et al. 2024

Information

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Fog computing (FC) is a distributed architecture in which computing resources and services are placed on edge devices closer to data sources. This enables more efficient data processing, shorter latency times, and better performance. Fog computing was shown to be a promising solution for addressing the new computing requirements. However, there are still many challenges to overcome to utilize this new computing paradigm, in particular, reliability and security. Following this need, a systematic literature review was conducted to create a list of requirements. As a result, the following four key requirements were formulated: (1) low latency and response times; (2) scalability and resource management; (3) fault tolerance and redundancy; and (4) privacy and security. Low delay and response can be achieved through edge caching, edge real-time analyses and decision making, and mobile edge computing. Scalability and resource management can be enabled by edge federation, virtualization and containerization, and edge resource discovery and orchestration. Fault tolerance and redundancy can be enabled by backup and recovery mechanisms, data replication strategies, and disaster recovery plans, with a residual number system (RNS) being a promising solution. Data security and data privacy are manifested in strong authentication and authorization mechanisms, access control and authorization management, with fully homomorphic encryption (FHE) and the secret sharing system (SSS) being of particular interest.

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A game-theoretic model for dynamic load balancing in distributed systems

Cited by 22 publications

References 9 publications

Geographically Distributed Load Balancing with (Almost) Arbitrary Load Functions

Geographically Distributed Load Balancing with (Almost) Arbitrary Load Functions

Round Robin with Load Degree: An Algorithm for Optimal Cloudlet Discovery in Mobile Cloud Computing

Reliablity and Security for Fog Computing Systems

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