Composable and predictable dynamic loading for time-critical partitioned systems on multiprocessor architectures

Abstract

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Dynamic software updating: a systematic mapping study

“…On the other hand, update points facets are less discussed facets in the domain.In this study, we have identified a list of 27 models for DSU. Based on the number of primary studies for each model, we list the top five most cited models in Table 11. Furthermore, the result shows other models including binary rewriting [53, 74 ], labelled transition systems [9, 69 ], incremental [83, 102 ], instantaneous [83, 102 ], mix objects object [42, 44 ], dynamic loading [97, 133 ], stack mapping [105 ], adaptation [64 ], aspect‐based [96 ], class‐loading [45 ], dynamic linking [53 ], relocatable code [53 ], event‐based [9 ], multiple consistency [44 ], object space [56 ], dynamic rebinding [5 ], representation consistency [77 ], shared library [53 ], stack reconstruction [74 ], strict consistency [87 ], V‐shape protocol [60 ], state machine [89 ], and component model [70 ]. RQ 3: What programming languages and algorithms employed in dynamic software updating?…”

“…For DSU time metrics, number of studies have presented various metrics including compile‐time [41, 44, 46, 54, 96 ], load time [41, 45, 60, 133 ], waiting time [51, 73, 76, 83 ], reconfiguration time [59, 60, 62 ], rollback time [51, 101 ], quiescence time [3, 58 ], timeout [9, 111 ], debug time [60 ], disruption time [4 ], down‐time [56 ], garbage collection time [44 ], interrupt time [38 ], synchronisation time [93 ], pausing time [42 ], restart time [60 ], and reconfiguration period [67 ], and warm‐up time [56 ].…”

“…Furthermore, the number of investigations have illustrated extra application domains including onboard software attitude orbit control [110 ], power plant controller system [63 ], high‐performance computing [47 ], braking applications [133 ], data destitution centric applications [121 ], scientific applications [50 ], intelligent field devices [98 ], self‐adaptive systems [91 ], safety‐critical embedded systems [53 ], critical software‐intensive systems [122 ], message‐passing programs [118 ], transportation or space applications [122 ], dynamic vehicle software [66 ], speed up automatic program repair [30 ], and enhance security and privacy in high availability energy management applications in smart cities [106 ].…”

“…Moreover, number of authors have demonstrated other 15 evaluation metrics including availability metrics [6, 37 ], memory usage [41, 136 ], number of violations [77, 81 ], memory footprint [22, 77 ], energy cost [54, 106 ], update cost [63 ], clock cycles for DSU model [93 ], duration of time‐division multiplexing slot in multiprocessor [133 ], measuring the impact changes on resource demands [41 ], patch size [14 ], number of errors [90 ], success‐rate [35 ], simulation of two threads to measure the number of accepted and/or rejected update requests in different workloads [134 ], run the old version in a standard JVM (Java Virtual Machine) to record some performance metric as a baseline [87 ], and before/during/after the update measures [65 ].…”

Cloud-based Design and Virtual Prototyping Environment for Embedded Systems

“…Application bundles contain a description of the required resources and the executable code. These bundles once placed at a specific memory location are processed by the multi-tile loader [17] that instantiates the new application in the running system. In the simulation model, data can be directly placed into the shared memory.…”