2,515 research outputs found
Parallel Load Balancing on Constrained Client-Server Topologies
We study parallel \emph{Load Balancing} protocols for a client-server
distributed model defined as follows.
There is a set \sC of clients and a set \sS of servers where each
client has
(at most) a constant number of requests that must be assigned to
some server. The client set and the server one are connected to each other via
a fixed bipartite graph: the requests of client can only be sent to the
servers in its neighborhood . The goal is to assign every client request
so as to minimize the maximum load of the servers.
In this setting, efficient parallel protocols are available only for dense
topolgies. In particular, a simple symmetric, non-adaptive protocol achieving
constant maximum load has been recently introduced by Becchetti et al
\cite{BCNPT18} for regular dense bipartite graphs. The parallel completion time
is \bigO(\log n) and the overall work is \bigO(n), w.h.p.
Motivated by proximity constraints arising in some client-server systems, we
devise a simple variant of Becchetti et al's protocol \cite{BCNPT18} and we
analyse it over almost-regular bipartite graphs where nodes may have
neighborhoods of small size. In detail, we prove that, w.h.p., this new version
has a cost equivalent to that of Becchetti et al's protocol (in terms of
maximum load, completion time, and work complexity, respectively) on every
almost-regular bipartite graph with degree .
Our analysis significantly departs from that in \cite{BCNPT18} for the
original protocol and requires to cope with non-trivial stochastic-dependence
issues on the random choices of the algorithmic process which are due to the
worst-case, sparse topology of the underlying graph
Parallel Load Balancing on constrained client-server topologies
We study parallel Load Balancing protocols for the client-server distributed model defined as follows. There is a set of n clients and a set
of n servers where each client has (at most) a constant number of requests that must be assigned to some server. The client set and the server one are connected to each other via a fixed bipartite graph: the requests of client v can only be sent to the servers in its neighborhood. The goal is to assign every client request so as to minimize the maximum load of the servers.
In this setting, efficient parallel protocols are available only for dense topologies. In particular, a simple protocol, named raes, has been recently introduced by Becchetti et al. [1] for regular dense bipartite graphs. They show that this symmetric, non-adaptive protocol achieves constant maximum load with parallel completion time
and overall work, w.h.p.
Motivated by proximity constraints arising in some client-server systems, we analyze raes over almost-regular bipartite graphs where nodes may have neighborhoods of small size. In detail, we prove that, w.h.p., the raes protocol keeps the same performances as above (in terms of maximum load, completion time, and work complexity, respectively) on any almost-regular bipartite graph with degree.
Our analysis significantly departs from that in [1] since it requires to cope with non-trivial stochastic-dependence issues on the random choices of the algorithmic process which are due to the worst-case, sparse topology of the underlying graph
Re-engineering jake2 to work on a grid using the GridGain Middleware
With the advent of Massively Multiplayer Online Games (MMOGs), engineers and
designers of games came across with many questions that needed to be answered such
as, for example, "how to allow a large amount of clients to play simultaneously on the
same server?", "how to guarantee a good quality of service (QoS) to a great number
of clients?", "how many resources will be necessary?", "how to optimize these resources
to the maximum?". A possible answer to these questions relies on the usage of grid
computing.
Taking into account the parallel and distributed nature of grid computing, we can say
that grid computing allows for more scalability in terms of a growing number of players,
guarantees shorter communication time between clients and servers, and allows for a
better resource management and usage (e.g., memory, CPU, core balancing usage, etc.)
than the traditional serial computing model.
However, the main focus of this thesis is not about grid computing. Instead, this
thesis describes the re-engineering process of an existing multiplayer computer game,
called Jake2, by transforming it into a MMOG, which is then put to run on a grid
SNAP: Stateful Network-Wide Abstractions for Packet Processing
Early programming languages for software-defined networking (SDN) were built
on top of the simple match-action paradigm offered by OpenFlow 1.0. However,
emerging hardware and software switches offer much more sophisticated support
for persistent state in the data plane, without involving a central controller.
Nevertheless, managing stateful, distributed systems efficiently and correctly
is known to be one of the most challenging programming problems. To simplify
this new SDN problem, we introduce SNAP.
SNAP offers a simpler "centralized" stateful programming model, by allowing
programmers to develop programs on top of one big switch rather than many.
These programs may contain reads and writes to global, persistent arrays, and
as a result, programmers can implement a broad range of applications, from
stateful firewalls to fine-grained traffic monitoring. The SNAP compiler
relieves programmers of having to worry about how to distribute, place, and
optimize access to these stateful arrays by doing it all for them. More
specifically, the compiler discovers read/write dependencies between arrays and
translates one-big-switch programs into an efficient internal representation
based on a novel variant of binary decision diagrams. This internal
representation is used to construct a mixed-integer linear program, which
jointly optimizes the placement of state and the routing of traffic across the
underlying physical topology. We have implemented a prototype compiler and
applied it to about 20 SNAP programs over various topologies to demonstrate our
techniques' scalability
Distributed Dynamic Replica Placement and Request Redirection in Content Delivery Networks
Chiara Petrioli, Giancarlo Bongiovanni, Francesco Lo Prest
- …