Deterministic Constant-Amortized-RMR Abortable Mutex for CC and DSM

The abortable mutual exclusion problem, proposed by Scott and Scherer in response to the needs in real-time systems and databases, is a variant of mutual exclusion that allows processes to abort from their attempt to acquire the lock. Worst-case constant remote memory reference algorithms for mutual exclusion using hardware instructions such as Fetch&Add or Fetch&Store have long existed for both cache coherent (CC) and distributed shared memory multiprocessors, but no such algorithms are known for abortable mutual exclusion. Even relaxing the worst-case requirement to amortized, algorithms are only known for the CC model. In this article, we improve this state of the art by designing a deterministic algorithm that uses Fetch&Store to achieve amortized O (1) remote memory reference in both the CC and distributed shared memory models. Our algorithm supports Fast Abort (a process aborts within six steps of receiving the abort signal) and has the following additional desirable properties: it supports an arbitrary number of processes of arbitrary names, requires only O (1) space per process, and satisfies a novel fairness condition that we call Airline FCFS . Our algorithm is short with fewer than a dozen lines of code.

Making weak memory models fair

Liveness properties, such as termination, of even the simplest shared-memory concurrent programs under sequential consistency typically require some fairness assumptions about the scheduler. Under weak memory models, we observe that the standard notions of thread fairness are insufficient, and an additional fairness property, which we call memory fairness, is needed. In this paper, we propose a uniform definition for memory fairness that can be integrated into any declarative memory model enforcing acyclicity of the union of the program order and the reads-from relation. For the well-known models, SC, x86-TSO, RA, and StrongCOH, that have equivalent operational and declarative presentations, we show that our declarative memory fairness condition is equivalent to an intuitive model-specific operational notion of memory fairness, which requires the memory system to fairly execute its internal propagation steps. Our fairness condition preserves the correctness of local transformations and the compilation scheme from RC11 to x86-TSO, and also enables the first formal proofs of termination of mutual exclusion lock implementations under declarative weak memory models.

Perceived organizational customer orientation and frontline employees' innovative behaviors: exploring the role of empowerment and supervisory fairness

PurposeThe purpose of this research is to examine the indirect effect of perceived organizational customer orientation on frontline employees' (FLE) innovative behaviors (via perceived empowerment) as well as the contextual factor of supervisory fairness, which affects the strength of the indirect effect. Drawing on social exchange theory, the authors propose that FLEs' perceived organizational customer orientation positively affects their empowerment and indirectly affects innovative behaviors, and that effect is stronger in a high supervisory fairness condition.Design/methodology/approachStructural equation modeling of the data collected through a time-lagged survey of 184 employee–supervisor dyads provides support for the hypotheses. From the practitioners' perspective, this study highlights the mechanism through which perceived organizational customer orientation can affect the display of FLEs' innovative behaviors as well as the conditions that strengthen this process.FindingsPerceived organizational customer orientation was positively related to employees' perceived empowerment. Empowerment was positively associated with supervisor-reported innovative behaviors. The indirect effect of perceived organizational customer orientation through employee empowerment on supervisor-reported innovative behaviors was also confirmed. Supervisory fairness significantly moderated the perceived organizational customer orientation and employee empowerment relationship. Finally, the indirect effect of customer orientation on supervisor-reported innovative behaviors through empowerment was significant for both high supervisory fairness and low supervisory fairness; however, the effect was stronger in a high fairness condition.Practical implicationsService managers can benefit from these findings by improving the work environment characteristics.Originality/valueThis study makes an important contribution to existing research on perceived organizational customer orientation and FLEs' innovative behaviors as extant research has only examined the direct unmediated effect of customer orientation on innovative behaviors. Moreover, the authors’ moderated mediation model presents a new insight into how perceived organizational customer orientation influences FLEs' innovative behaviors and when this effect is more pronounced.

A weighted resource discovery approach in grid computing

Purpose Grid computing is an effective environment for the execution of parallel applications that requires great computing power. This paper aims to present, based on the hierarchical architecture, an improved weighted resource discovery (WRD) algorithm to manage allocation of resources and minimize cost of communications between grid nodes. Design/methodology/approach A behavioral modeling method is addressed to prove the proposed method correctness. The behavioral model of the proposed algorithm is implemented by StarUML tool with two different model-checking mechanisms. Then, the resource discovery correctness is analyzed in terms of reachability condition, fairness condition and deadlock-free using NuSMV model checker. Findings The results show that WRD algorithm has better performance in requiring re-discovery process, the number of examined nodes in each request and discovering the free resources with high-bandwidth links. Originality/value To store information of resources, a new data structure called resource information table is proposed which facilitates resource finding of the algorithm. A behavioral modeling method is addressed to prove the proposed method correctness.

A call-by-need lambda calculus with locally bottom-avoiding choice: context lemma and correctness of transformations

We present a higher-order call-by-need lambda calculus enriched with constructors, case expressions, recursive letrec expressions, a seq operator for sequential evaluation and a non-deterministic operator amb that is locally bottom-avoiding. We use a small-step operational semantics in the form of a single-step rewriting system that defines a (non-deterministic) normal-order reduction. This strategy can be made fair by adding resources for book-keeping. As equational theory, we use contextual equivalence (that is, terms are equal if, when plugged into any program context, their termination behaviour is the same), in which we use a combination of may- and must-convergence, which is appropriate for non-deterministic computations. We show that we can drop the fairness condition for equational reasoning, since the valid equations with respect to normal-order reduction are the same as for fair normal-order reduction. We develop a number of proof tools for proving correctness of program transformations. In particular, we prove a context lemma for both may- and must- convergence that restricts the number of contexts that need to be examined for proving contextual equivalence. Combining this with so-called complete sets of commuting and forking diagrams, we show that all the deterministic reduction rules and some additional transformations preserve contextual equivalence. We also prove a standardisation theorem for fair normal-order reduction. The structure of the ordering ≤c is also analysed, and we show that Ω is not a least element and ≤c already implies contextual equivalence with respect to may-convergence.

MAX-MIN FAIR FLOW CONTROL SENSITIVE TO PRIORITIES

Flow control is the dominant technique currently used in communication networks for preventing excess traffic from flooding the network, and for handling congestion. In rate-based flow control, transmission rates of sessions are adjusted in an end-to-end manner through a sequence of operations. In this work, we present a theory of max-min fair, rate-based flow control sensitive to priorities of different sessions, as a significant extension of the classical theory of max-min fair, rate-based flow control to networks supporting applications with diverse requirements on network resources. Each individual session bears a priority function, which maps the session's priority to a transmission rate; the priority is a working abstraction of the session's priority to bandwidth access. Priority functions enable the specification of requirements on bandwidth access by distributed applications, and the formal handling of such requirements. We present priority max-min fairness, as a novel and well motivated fairness condition which requires that assigned rates correspond, through the priority functions, to priorities comprising a max-min vector. We also introduce priority bottleneck algorithms gradually update a session's rate until when its priority is restricted on a priority bottleneck edge of the network. We establish a collection of interesting combinatorial properties of priority bottleneck algorithms. Most significantly, we show that they can only converge to priority max-min fairness. As an application of our general theory, we embed priority bottleneck algorithms in the more realistic optimistic framework for rate-based flow control. The optimistic framework allows for both decreases and increases of session rates. We exploit these additionally provided semantics to prove further combinatorial properties for the termination of priority bottleneck algorithms in the optimistic framework. We use these properties to conclude the first optimistic algorithms for efficient, max-min fair, rate-based flow control sensitive to priorities.