An Efficient Scheduling Policy for Load Balancing Model for Computational Grid System

Workload and resource management are two essential functions provided at the service level of the Grid system. To improvement in global throughput need, effective and efficient load balancing are fundamentally important. We also check that what type of scheduling policy is used by that algorithm, because an efficient scheduling policy can utilize the computational resources efficiently by allowing multiple independent jobs to run over a network of heterogeneous clusters. In this paper, a dynamic grid model, as a collection of clusters has been proposed. An efficient scheduling policy is used, and its comparison with the other scheduling policy has been presented

Алгоритми балансування навантаження в Грід-системах

Проведено аналіз стратегій балансування навантаження в Грід-системах та порівняльний аналіз наявних алгоритмів розподілу потоку завдань між обчислювальними ресурсами Грід-середовища.Проведен анализ стратегий балансирования нагрузки в Грид-системах и сравнительный анализ имеющихся алгоритмов распределения потока заданий между вычислительными ресурсами Грид-среды.The analysis of strategies of balancing load in the Grid-systems and comparative analysis of existing algorithms of the distribution of flow of the tasks between computing resources of a Grid environment are shown

Алгоритмы балансирования нагрузки в Грид-системах

Проведено аналіз стратегій балансування навантаження в Грід-системах та порівняльний аналіз наявних алгоритмів розподілу потоку завдань між обчислювальними ресурсами Грід-середовища.The analysis of strategies of balancing load in the Grid-systems and comparative analysis of existing algorithms of the distribution of flow of the tasks between computing resources of a Grid environment are shown.Проведен анализ стратегий балансирования нагрузки в Грид-системах и сравнительный анализ имеющихся алгоритмов распределения потока заданий между вычислительными ресурсами Грид-среды

Content-based addressing in hierarchical distributed hash tables

Peer-to-peer networks have drawn their strength from their ability to operate functionally without the use of a central agent. In recent years the development of the structured peer-to-peer network has further increased the distributed nature of p2p systems. These networks take advantage of an underlying distributed data structure, a common one is the distributed hash table (DHT). These peers use this structure to act as equals in a network, sharing the same responsibilities of maintaining and contributing. But herein lays the problem, not all peers are equal in terms of resources and power. And with no central agent to monitor and balance load , the heterogeneous nature of peers can cause many distribution or bottleneck issues on the network and peer levels. This is due to the way in which addresses are allocated in these DHTs. Often this function is carried out by a consistent hashing function. These functions although powerful in their simplicity and effectiveness are the stem of a crucial flaw. This flaw causes the random nature in which addresses are assigned both when considering peer identification and allocating resource ownership. This work proposes a solution to mitigate the random nature of address assignment in DHTs, leveraging two methodologies called hierarchical DHTs and content based addressing. Combining these methods would enable peers to work in cooperative groups of like interested peers in order to dynamically share the load between group members. Group formation and utilization relies on the actual resources a peer willingly shares and is able to contribute rather than a function of the random hash employed by traditional DHT p2p structures

Decentralized algorithms using both local and random probes for p2p load balancing

We study randomized algorithms for placing a sequence of n nodes on a circle with unit perimeter. Nodes divide the circle into disjoint arcs. We desire that a newly-arrived node (which is oblivious of its index in the sequence) choose its position on the circle by learning the positions of as few existing nodes as possible. At the same time, we desire that that the variation in arc-lengths be small. To this end, we propose a new algorithm that works as follows: The k th node chooses r random points on the circle, inspects the sizes of v arcs in the vicinity of each random point, and places itself at the mid-point of the largest arc encountered. We show that for any combination of r and v satisfying rv ≥ c log k, where c is a small constant, the ratio of the largest to the smallest arc-length is at most eight w.h.p., for an arbitrarily lon

Load Balancing with Dynamic Set of Balls and Bins

In dynamic load balancing, we wish to distribute balls into bins in an environment where both balls and bins can be added and removed. We want to minimize the maximum load of any bin but we also want to minimize the number of balls and bins affected when adding or removing a ball or a bin. We want a hashing-style solution where we given the ID of a ball can find its bin efficiently. We are given a balancing parameter $c=1+\epsilon$, where $\epsilon\in (0,1)$. With $n$ and $m$ the current numbers of balls and bins, we want no bin with load above $C=\lceil c n/m\rceil$, referred to as the capacity of the bins. We present a scheme where we can locate a ball checking $1+O(\log 1/\epsilon)$ bins in expectation. When inserting or deleting a ball, we expect to move $O(1/\epsilon)$ balls, and when inserting or deleting a bin, we expect to move $O(C/\epsilon)$ balls. Previous bounds were off by a factor $1/\epsilon$. These bounds are best possible when $C=O(1)$ but for larger $C$, we can do much better: Let $f=\epsilon C$ if $C\leq \log 1/\epsilon$, $f=\epsilon\sqrt{C}\cdot \sqrt{\log(1/(\epsilon\sqrt{C}))}$ if $\log 1/\epsilon\leq C<\tfrac{1}{2\epsilon^2}$, and $C=1$ if $C\geq \tfrac{1}{2\epsilon^2}$. We show that we expect to move $O(1/f)$ balls when inserting or deleting a ball, and $O(C/f)$ balls when inserting or deleting a bin. For the bounds with larger $C$, we first have to resolve a much simpler probabilistic problem. Place $n$ balls in $m$ bins of capacity $C$, one ball at the time. Each ball picks a uniformly random non-full bin. We show that in expectation and with high probability, the fraction of non-full bins is $\Theta(f)$. Then the expected number of bins that a new ball would have to visit to find one that is not full is $\Theta(1/f)$. As it turns out, we obtain the same complexity in our more complicated scheme where both balls and bins can be added and removed.Comment: Accepted at STOC'2

Enabling technologies for decentralized interpersonal communication

In the recent years the Internet users have witnessed the emergence of Peer-to-Peer (P2P) technologies and applications. One class of P2P applications is comprised of applications that are targeted for interpersonal communication. The communication applications that utilize P2P technologies are referred to as decentralized interpersonal communication applications. Such applications are decentralized in a sense that they do not require assistance from centralized servers for setting up multimedia sessions between users. The invention of Distributed Hash Table (DHT) algorithms has been an important, but not an inclusive enabler for decentralized interpersonal communication. Even though the DHTs provide a basic foundation for decentralization, there are still a number of challenges without viable technological solutions. The main contribution of this thesis is to propose technological solutions to a subset of the existing challenges. In addition, this thesis also presents the preliminary work for the technological solutions. There are two parts in the preliminary work. In the first part, a set of DHT algorithms are evaluated from the viewpoint of decentralized interpersonal communication, and the second part gives a coherent presentation of the challenges that a decentralized interpersonal communication application is going to encounter in mobile networks. The technological solution proposals contain two architectures and two algorithms. The first architecture enables an interconnection between a decentralized and a centralized communication network, and the second architecture enables the decentralization of a set of legacy applications. The first algorithm is a load balancing algorithm that enables good scalability, and the second algorithm is a search algorithm that enables arbitrary searches. The algorithms can be used, for example, in DHT-based networks. Even though this thesis has focused on the decentralized interpersonal communication, some of the proposed technological solutions also have general applicability outside the scope of decentralized interpersonal communication